A collaborative methodology for developing a semantic model for interlinking Cancer Chemoprevention linked-data sources

Abstract

This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a "meet-in-the-middle" approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing interconnected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i) concepts from the literature that refer to cancer chemoprevention, ii) facts and resources relevant for cancer prevention, iii) collections of experimental data, procedures and protocols and iv) concepts to facilitate the representation of results related to virtual screening of chemopreventive agents.

Raising interoperability among base registries: The evolution of the Linked Base Registry for addresses in Flanders

“…The OSLO-program, which started as a grassroots initiative at the level of the local governments 20 , increased awareness on the need for semantic interoperability. OSLO created an ontology in three main domains of interest: (i) persons and organisations [15], (ii) locations, and (iii) public services 21 , in a setting where stakeholders are focusing on their similarities rather than on their differences [56]. This was achieved by implementing a process and methodology for developing semantic agreements, based on the ISA methodology [29].…”

“…We expect this will avoid shadow databases, containing redundant and outdated information. OSLO is aligning the vocabularies of the different base registries, which are part 56 http://data.vlaanderen.be/ns/ 57 https://www.w3.org/TR/json-ld-syntax/#the-context 58 https://www.w3.org/TR/json-ld-syntax/#interpreting-json-as-json-ld Fig. 13.…”

Introducing the Big Knowledge to Use (BK2U) challenge

“…For a framework offering support for scaling QA to large families of similar ontologies, see Ochs et al 35 Summaries and their visualizations have been algorithmically constructed for SNOMED CT, 18 NCIt, 17 NDF-RT, 19 GO, 12 OCRe, 36 SDO, 37 DDI, 38 and CanCo. 39 Our software systems (e.g., the Biomedical Layout Utility for SNOMED CT 40 and the Ontology Abstraction Framework 41 ) generate visual displays of the summaries of such terminologies. We used these displays for QA as well as for obtaining the big picture of these terminologies.…”

Taxonomy-Based Approaches to Quality Assurance of Ontologies

“…A variety of taxonomy-based methodologies have been developed for and utilized in SNOMED CT, the NCIt, and the GO. Additional methodologies have targeted ontologies in the NCBO's BioPortal [ 20 , 21 ] including the Ontology of Clinical Research (OCRe) [ 22 ], the Sleep Domain Ontology (SDO) [ 23 ], the Cancer Chemoprevention Ontology (CanCO) [ 24 ], and the Ontology for Drug Discovery Investigations (DDI) [ 25 ]. And now with a movement toward methodologies encompassing all the ontologies in a family of similar ontologies hosted in the BioPortal [ 26 ], it is important to survey the known methodologies that are taxonomy-based so various curators and editors can more readily use them.…”